A Sober Look at Agentic Misalignment in Automated Workflows
We study a class of emergent misalignment in multi-agent systems (MAS), with a focus on automated workflows, which we refer to agentic misalignment. Although these systems can solve complex tasks, they often fail because agents act according to implicit proxy utilities that do not align with the intended human goals. We formally define these behaviors and analyze them within a Bayesian framework, showing that generic utilities naturally lead to posterior collapse of agents in automated workflows. To address this issue, we propose Agentic Evidence Attribution (AEA), a novel alignment paradigm that improves agent posteriors using context-specific evidence. AEA reasons over agent actions and...
| Authors | Wenqian Ye, Bo Yuan, Zhichao Xu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 May 2026 |
| Last updated at source | 8 June 2026 |
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